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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

共 2097 条记录，以下是61-70 订阅

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Research on the technology of the Multi-operating system Co-development Based on the Unity Platform 3

Research on the Technology of the Multi-operating System Co-...

引用

3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Hu, Xi Shen, Zhiwei Chen, Zhiqi Ran, Xiaoyue Xiong, Xin Wu, You Jianghan University School of Artificial Intelligence Wuhan China Education Technology Co. Ltd Wuhan Chaoxing Digital Library Wuhan China Hubei University School of Computer Science and Information Engineering Wuhan China

ISBN: (纸本)9798350398410

Currently, the technology of the multi-operating system co-development has been widely used, which shows great advantages in simplifying technical requirements and reducing costs. A Unity platform is the platform that is able to support 28 platform games and be published on the multiple platforms during compiling at one time. Aiming at the duplication problem of the workloads in the multiple operating system, by considering the traditional research method of the MVC model-based software and the research technology of the Unity3D platform software, this paper proposes system detection and system adaptation algorithms, for researching on the technology of the multiple operating systems. By means of detecting the current system, the description field of this system can be obtained by the system detection algorithm. Based on the system description field, the specific module at compile time is adopted by the system adaptation algorithm, which can solve the differences in the implementation of the different operating system functions. This paper combines with a specific development project case, describes the algorithm implementation method, explains the technical difficulties of our algorithm in practice paper, enabled it to be successfully released on both Windows and Android platforms, and finally verifies the feasibility of the technology. © 2023 IEEE.

关键词： Signal detection

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An AI Based Electric Field detection using UAV 3

An AI Based Electric Field Detection using UAV

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Padghan, Milind N Bansod, Saurabh Singh, Shashank Kumar Pal, Prasahnt National Institute of Electronics and Information Technology Department of EDT Aurangabad India National Institute of Electronics and Information Technology Aurangabad India

ISBN: (纸本)9781665494007

The Idea of detecting the Electric Field which is sustained in the space universe will get detected in this system. The implementation of Unmanned Aerial Vehicle with autonomous control can specifically determine the system of its intensity, levels of deficiency, benefits and circuitry difficulties in the field when synchronizing with Artificial intelligence. The traditional aircraft systems have a manual pilot operator to maintain security and safety, the charge existed in the medium can proportionally detect the quantum of charge, intensity, waveform of representation. This proposed framework consists of a decentralized control in architecture which simultaneously controls the monitors and the swarms of resource-constraint in UAV for their real-time requirements. The outcomes of UAVs can be mitigated significantly in space medium and their efficiency can be increased in realizing their diverse range of missions. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Application of Computer Simulation technology in detection Model of Mycoplasma Pathogen Subtype 3

Application of Computer Simulation Technology in Detection M...

引用

3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Ling, He Erlong, Cao Xiao, Tan Shaoyang University Shaoyang China

ISBN: (纸本)9798350398410

The traditional Microplastic-based microbial invasion detection system uses a low-level binary detector. An algorithm is proposed to judge whether the random pattern has vulnerabilities based on the in-depth study of the detection technology of mycoplasma pathogen microorganism invasion. A new model of mycoplasma pathogen invasion detection system was designed based on the above research,. The affinity maturation process, memory detector variation and incomplete matching rules were introduced in the model. In this paper, the hybrid intrusion detection system of mycoplasma pathogenic microorganisms based on immunological principle was used to conduct intrusion detection experiments, and the average detection rate of all mycoplasma pathogenic microorganisms' attack types was 94.94%. © 2023 IEEE.

关键词： Intrusion detection

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Performance Analysis of YOLO-DeepSORT on Thermal Video-Based Online Multi-object Tracking 3

Performance Analysis of YOLO-DeepSORT on Thermal Video-Based...

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3rd international conference on Robotics, Automation and Artificial intelligence (RAAI)

作者： Ibrahim, Nur Darlis, Arsyad Ramadhan Herianto Kusumoputro, Benyamin Univ Indonesia Fac Engn Dept Elect Engn Res Ctr Artificial Intelligence & Data Engn Depok Indonesia Darma Persada Univ Fac Engn Informat Syst Study Program Jakarta Indonesia

ISBN: (纸本)9798350307573;9798350307566

Recently, thermal cameras have been used in various fields, including surveillance systems and advanced driver assistance systems (ADAS), as they perform better in low light than visible-light cameras. Some challenges in the surveillance system or ADAS field related to thermal cameras are occlusion and thermal crossover between objects with similar appearances during object detection or object tracking tasks, which can lead to misdetection, false positives, and lost tracking. In this paper, performance analysis of you-only-look-once (YOLO) combined with deep online real-time tracking (DeepSORT) on thermal video-based online multi-object tracking (MOT) in occlusion and thermal crossover scene is presented. YOLO, as one of state-ofthe-art method for detection task, is used for detection system. Then, the detected object from YOLO is tracked using DeepSORT. The results demonstrate that the online MOT of sequential thermal images using YOLO-DeepSORT achieved a MOTA score of 44.2% and IDF1 of 45.3%. Thus, negative example was added in YOLO training process to reduce false detection, and it gives improvement with MOTA score of 63.8% and IDF1 score of 54.6%.

关键词： YOLO DeepSORT online multi-object tracking thermal image negative example

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Credit Card Fraud detection using Neural Embeddings and Radial Basis Network with a novel hybrid fruitfly-fireworks algorithm 3

Credit Card Fraud Detection using Neural Embeddings and Radi...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Singh, Indu Aditya, Nilesh Srivastava, Pranjal Mittal, Shivanshu Mittal, Taruvar Surin, Noel Vaibhav Delhi Technological University Department of Computer Science and Engineering India Netaji Subhas University of Technology Department of Manufacturing Processes and Automation Engineering India Netaji Subhas University of Technology Department of Information Technology India

ISBN: (纸本)9798350338607

Financial institutions are highly concerned about credit card fraud due to the growing frequency of deceitful activities linked with credit card transactions. To address this issue, a novel fraud detection system is proposed that combines advanced feature engineering and neural network architecture. The proposed approach. FFFW-RBN, employs a hybrid swarm intelligence model comprising the Fruitfly Optimization Algorithm and Fireworks Algorithm, (FFO-FWA) for feature selection, and the Neural Embeddings Generator in combination with a Shallow Radial Basis Network (RBF) for localized pattern classification. The proposed system achieves a 99.97% accuracy, 0.91 recall, and a precision of 0.94. The experimental results demonstrate that our methodology effectively and practically detects fraudulent credit card transactions. The paper presents a comprehensive evaluation of the system, including various performance metrics and a confusion matrix, highlighting its potential for real-world implementation. © 2023 IEEE.

关键词： Radial basis function networks

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2023 3rd international conference on Applied Artificial intelligence, ICAPAI 2023

2023 3rd International Conference on Applied Artificial Inte...

引用

3rd international conference on Applied Artificial intelligence, ICAPAI 2023

ISBN: (纸本)9798350328929

The proceedings contain 11 papers. The topics discussed include: metaheuristic firefly and C5.0 algorithms based intrusion detection for critical infrastructures;a novel spatial-temporal deep neural network for electricity price forecasting;temporal-spatial graph neural network for wind power forecasting considering the blockage effects;European standardization efforts from fair toward explainable-AI-ready data documentation in materials modelling;detection of pneumonia from chest X-ray images using convolutional neural network (CNN);SVM at edge: low cost caching prediction for connected edge intelligence in federated machine learning;staying ahead of the curve: early prediction of academic probation among first-year cs students;facial expression recognition using deep neural network;human posture detection on lightweight DCNN and SVM in a digitalized healthcare system;and fishing trawler event detection: an important step towards digitization of sustainable fishing.

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A Scalable Network Intrusion detection system using Bi-LSTM and CNN 3

A Scalable Network Intrusion Detection System using Bi-LSTM ...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Kanumalli, Satya Sandeep Lavanya, K. Rajeswari, A. Samyuktha, P. Tejaswi, M. Vignan's Nirula Institute of Technology and Science for Women Department of Computer Sc. and Engineering Guntur India

ISBN: (纸本)9781665462167

As cloud technologies are used more frequently, network intrusion detection systems are becoming increasingly well-liked. Due to ever-increasing network traffic and the regular emergence of new types of assaults, Network Intrusion detection (NIDS) came into existence as a key aspect of network security and must be extremely effective. These kind of IDS systems employ either an anomaly detection system based on machine learning or a system for matching patterns. The False Positive Rate for pattern matching approaches is high, but AI/ML-based systems determine the possibility of an attack by identifying a metric or characteristic or a connection between a number of metrics or characteristics. The most popular models include KNN, SVM, and others, they only work on a small range of traits, are not very accurate, and have a high False Positive Rate. This study created a deep learning system to learn the temporal and spatial data properties using the advantages of CNN and Bidirectional LSTM. The system present in this paper is trained and analyzed using the openly available dataset NSL-KDD. The proposed model has a high rate of detection and a low incidence of false positives. A lot of cutting-edge Network Intrusion detection systems that use Machine Learning/Deep Learning models perform better than the suggested model. © 2023 IEEE.

关键词： Decision trees

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Design of Automatic Game system Based on Artificial intelligence for Three Pieces of Chess 3

Design of Automatic Game System Based on Artificial Intellig...

引用

3rd international conference on Artificial intelligence, Internet of Things and Cloud Computing technology, AIoTC 2024

作者： Li, Ziwen Lin, Dun Deng, Kaitao Li, Weijia Yang, Jiale Zhang, Lei College of Electronic Information Engineering Zhuhai College of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798331530280

In this paper, we design an automatic three pieces game device based on STM32F407ZGT6, aiming at realizing the accurate human-computer interaction game function. The device utilizes an air pump and X, Y gantry mechanical structure and integrates YOLOv5 deep learning model. Equipped with Emm42_V5.0 closed-loop stepper motors and MG996R servo motors, the device can precisely control the placement of the pieces in 3D space. By connecting to MaixCAM to realize piece position detection and confirmation, ensuring accurate execution of the game. the introduction of the YOLOv5 model significantly improves the accuracy and speed of piece recognition, enabling the system to respond to user actions and game changes in a timely manner. Provides a friendly user interface with physical key input. Users can notify the system to start the timer by pressing the designated button after completing a round of piece placement. Meanwhile, feedback to the user after each round of the game is provided through an indicator light, which facilitates real-time monitoring of the progress of the game. © 2024 IEEE.

关键词： Stepping motors

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Research on Aero-Engine detection system Based on Computer Virtual Reality technology 3

Research on Aero-Engine Detection System Based on Computer V...

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3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Guo, Qingqing Liu, Liya Sanya Aviation and Tourism College Sanya China

ISBN: (纸本)9798350398410

This paper designs and develops a set of portable comprehensive detection system based on virtual instrument technology. The detection system consists of laptop computer, engine parameter recorder, dynamic test data acquisition device and other hardware. The upper computer communicates with the lower computer through the 422 buses. With the corresponding automatic detection software, the scanning path setting, automatic sensor probe scanning, detection data preprocessing and microdefect imaging can be realized. The miniaturization, modularization and automation of the high precision measurement and control system are realized by using all digital design, improving the signal conditioning circuit of DDS technology and using virtual instrument technology. The test results show that the system can accurately reflect the test parameters, working conditions and test curves of the tested aero-engine. © 2023 IEEE.

关键词： Data acquisition

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A Self-Powered Sensing system with Embedded TinyML for Anomaly detection 3

A Self-Powered Sensing System with Embedded TinyML for Anoma...

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3rd IEEE international conference on Industrial Electronics for Sustainable Energy systems, IESES 2023

作者： Chen, Zijie Gao, Yiming Liang, Junrui School of Information Science and Technology ShanghaiTech University Shanghai201210 China

ISBN: (纸本)9798350324754

In the coming Industry 4.0 era, IoT (Internet of Things) technology plays a more and more critical role. Anomaly detection is one of the essential applications for ensuring safe manufacturing, living, etc. It equips the machine with intelligence for perceiving its situation in real-time to prevent disastrous breakdown. Such monitoring systems were usually based on commercial inertial measurement units (IMU) and artificial intelligence (AI) algorithms that run on resource-rich, power-hungry servers, causing a certain amount of energy for sensing and data transmitting. In this paper, we introduce a novel self-powered sensing system with tiny machine learning (Tiny ML) technique for anomaly detection. A lightweight piezoelectric self-powered sensor (SPS) is utilized to substitute the IMU. The system runs on a low-cost embedded system, realizing the low-power in-situ inferring. A rich dataset has been collected on a vibration platform and analyzed by six well-known machine-learning models. A compressed deep neuron network (DNN) with three hidden layers achieves an accuracy of 97.6 % given only 8-point normalized SPS data. The TinyML model is then deployed on embedded systems for on-device inferring and condition-based monitoring. Power measurement is conducted to compare the systems based on an IMU and an SPS. It has shown that the proposed self-powered sensing approach can save up to 66.74% of energy. The system provides a valuable reference for realizing pervasive sensing and ubiquitous AI. © 2023 IEEE.

关键词： Anomaly detection

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